Details
| Original language | English |
|---|---|
| Pages (from-to) | 334-340 |
| Number of pages | 7 |
| Journal | NEURAL NETWORKS |
| Volume | 146 |
| Early online date | 1 Dec 2021 |
| Publication status | Published - Feb 2022 |
Abstract
In neurological and neuropsychiatric disorders neuronal oscillatory activity between basal ganglia and cortical circuits are altered, which may be useful as biomarker for adaptive deep brain stimulation. We investigated whether changes in the spectral power of oscillatory activity in the motor cortex (MCtx) and the sensorimotor cortex (SMCtx) of rats after injection of the dopamine (DA) receptor antagonist haloperidol (HALO) would be similar to those observed in Parkinson disease. Thereafter, we tested whether a convolutional neural network (CNN) model would identify brain signal alterations in this acute model of parkinsonism. A sixteen channel surface micro-electrocorticogram (ECoG) recording array was placed under the dura above the MCtx and SMCtx areas of one hemisphere under general anaesthesia in rats. Seven days after surgery, micro ECoG was recorded in individual free moving rats in three conditions: (1) basal activity, (2) after injection of HALO (0.5 mg/kg), and (3) with additional injection of apomorphine (APO) (1 mg/kg). Furthermore, a CNN-based classification consisting of 23,530 parameters was applied on the raw data. HALO injection decreased oscillatory theta band activity (4–8 Hz) and enhanced beta (12–30 Hz) and gamma (30–100 Hz) in MCtx and SMCtx, which was compensated after APO injection (P ¡ 0.001). Evaluation of classification performance of the CNN model provided accuracy of 92%, sensitivity of 90% and specificity of 93% on one-dimensional signals. The CNN proposed model requires a minimum of sensory hardware and may be integrated into future research on therapeutic devices for Parkinson disease, such as adaptive closed loop stimulation, thus contributing to more efficient way of treatment.
Keywords
- Acute rat model, Convolutional Neural Network, Deep learning, Electrocorticogram, Electroencephalogram, Parkinson's disease
ASJC Scopus subject areas
- Neuroscience(all)
- Cognitive Neuroscience
- Computer Science(all)
- Artificial Intelligence
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In: NEURAL NETWORKS, Vol. 146, 02.2022, p. 334-340.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Predictive accuracy of CNN for cortical oscillatory activity in an acute rat model of parkinsonism
AU - Ali, Ali Abdul Nabi
AU - Alam, Mesbah
AU - Klein, Simon
AU - Behmann, Nicolai
AU - Krauss, Joachim K.
AU - Doll, Theodor
AU - Blume, Holger
AU - Schwabe, Kerstin
N1 - Funding Information: The authors gratefully acknowledge the partial funding of this work by German BMBF , grant number 13GW0050B .
PY - 2022/2
Y1 - 2022/2
N2 - In neurological and neuropsychiatric disorders neuronal oscillatory activity between basal ganglia and cortical circuits are altered, which may be useful as biomarker for adaptive deep brain stimulation. We investigated whether changes in the spectral power of oscillatory activity in the motor cortex (MCtx) and the sensorimotor cortex (SMCtx) of rats after injection of the dopamine (DA) receptor antagonist haloperidol (HALO) would be similar to those observed in Parkinson disease. Thereafter, we tested whether a convolutional neural network (CNN) model would identify brain signal alterations in this acute model of parkinsonism. A sixteen channel surface micro-electrocorticogram (ECoG) recording array was placed under the dura above the MCtx and SMCtx areas of one hemisphere under general anaesthesia in rats. Seven days after surgery, micro ECoG was recorded in individual free moving rats in three conditions: (1) basal activity, (2) after injection of HALO (0.5 mg/kg), and (3) with additional injection of apomorphine (APO) (1 mg/kg). Furthermore, a CNN-based classification consisting of 23,530 parameters was applied on the raw data. HALO injection decreased oscillatory theta band activity (4–8 Hz) and enhanced beta (12–30 Hz) and gamma (30–100 Hz) in MCtx and SMCtx, which was compensated after APO injection (P ¡ 0.001). Evaluation of classification performance of the CNN model provided accuracy of 92%, sensitivity of 90% and specificity of 93% on one-dimensional signals. The CNN proposed model requires a minimum of sensory hardware and may be integrated into future research on therapeutic devices for Parkinson disease, such as adaptive closed loop stimulation, thus contributing to more efficient way of treatment.
AB - In neurological and neuropsychiatric disorders neuronal oscillatory activity between basal ganglia and cortical circuits are altered, which may be useful as biomarker for adaptive deep brain stimulation. We investigated whether changes in the spectral power of oscillatory activity in the motor cortex (MCtx) and the sensorimotor cortex (SMCtx) of rats after injection of the dopamine (DA) receptor antagonist haloperidol (HALO) would be similar to those observed in Parkinson disease. Thereafter, we tested whether a convolutional neural network (CNN) model would identify brain signal alterations in this acute model of parkinsonism. A sixteen channel surface micro-electrocorticogram (ECoG) recording array was placed under the dura above the MCtx and SMCtx areas of one hemisphere under general anaesthesia in rats. Seven days after surgery, micro ECoG was recorded in individual free moving rats in three conditions: (1) basal activity, (2) after injection of HALO (0.5 mg/kg), and (3) with additional injection of apomorphine (APO) (1 mg/kg). Furthermore, a CNN-based classification consisting of 23,530 parameters was applied on the raw data. HALO injection decreased oscillatory theta band activity (4–8 Hz) and enhanced beta (12–30 Hz) and gamma (30–100 Hz) in MCtx and SMCtx, which was compensated after APO injection (P ¡ 0.001). Evaluation of classification performance of the CNN model provided accuracy of 92%, sensitivity of 90% and specificity of 93% on one-dimensional signals. The CNN proposed model requires a minimum of sensory hardware and may be integrated into future research on therapeutic devices for Parkinson disease, such as adaptive closed loop stimulation, thus contributing to more efficient way of treatment.
KW - Acute rat model
KW - Convolutional Neural Network
KW - Deep learning
KW - Electrocorticogram
KW - Electroencephalogram
KW - Parkinson's disease
UR - http://www.scopus.com/inward/record.url?scp=85121244921&partnerID=8YFLogxK
U2 - 10.1016/j.neunet.2021.11.025
DO - 10.1016/j.neunet.2021.11.025
M3 - Article
VL - 146
SP - 334
EP - 340
JO - NEURAL NETWORKS
JF - NEURAL NETWORKS
SN - 0893-6080
ER -